With the abundance of multimedia data made available through various means in general and the Internet and world-wide-web (WWW) in particular, there is a need for effective ways of searching for, and management of such multimedia data. Searching, organizing, and managing multimedia data in general and video data in particular may be challenging at best due to the difficulty of representing and comparing the information embedded in the video content, and due to the scale of information that needs to be checked. Moreover, when it is necessary to find a content of video by means of textual query, prior art cases revert to various metadata solutions that textually describe the content of the multimedia data. However, such content may be abstract and complex by nature and not necessarily adequately defined by the existing and/or attached metadata.
The rapid increase in multimedia databases, accessible for example through the Internet, calls for the application of new methods of representation of information embedded in video content. Searching for multimedia in general and for video data in particular is challenging due to the huge amount of information that has to be priority indexed, classified and clustered. Moreover, prior art techniques revert to model-based methods to define and/or describe multimedia data. However, by its very nature, the structure of such multimedia data may be too abstract and/or complex to be adequately represented by means of metadata. The difficulty arises in cases where the target sought for multimedia data is not adequately defined in words, or by respective metadata of the multimedia data. For example, it may be desirable to locate a car of a particular model in a large database of video clips or segments. In some cases the model of the car would be part of the metadata but, in many cases it would not. Moreover, the car may be at angles different from the angles of a specific photograph of the car that is available as a search item. Similarly, if a piece of music, as in a sequence of notes, is to be found, it is not necessarily the case that in all available content the notes are known in their metadata form, or for that matter, the search pattern may just be a brief audio clip.
A system implementing a computational architecture (hereinafter “the Architecture”) that is based on a PCT patent application publication number WO2007/049282 and published on May 3, 2007, entitled “A Computing Device, a System and a Method for Parallel Processing of Data Streams”, assigned to common assignee, is hereby incorporated by reference for all the useful information it contains. Generally, the Architecture consists of a large ensemble of randomly, independently generated, heterogeneous processing cores, mapping in parallel data-segments onto a high-dimensional space and generating compact signatures for classes of interest.
Searching multimedia data has been a challenge of past years and has therefore received considerable attention. Early systems would take a multimedia data element in the form of, for example, an image, compute various visual features from it, and then search one or more indexes to return images with similar features. In addition, values for these features and appropriate weights reflecting their relative importance could also be used. Searching and indexing techniques have improved over time to handle various types of multimedia inputs and handle them with ever increasing effectiveness. However, subsequent to the exponential growth of the use of the Internet and the multimedia data available there, these prior art systems have become less effective in handling the multimedia data, due to the vast amounts already existing, as well as the speed at which new ones are added.
Searching has therefore become a significant challenge and even the addition of metadata to assist in the search has limited functionality. First, metadata may be inaccurate or not fully descriptive of the multimedia data, and second, not every piece of multimedia data can be accurately enough described by a sequence of textual metadata. A query model for a search engine has some advantages, such as comparison and ranking of images based on objective visual features, rather than on subjective image annotations. However, the query model has its drawbacks as well. Certainly when no metadata is available and only the multimedia data needs to be used, the process requires significant effort. Those skilled in the art will appreciate that there is no known intuitive way of describing multimedia data. Therefore, a large gap may be found between a user's perception or conceptual understanding of the multimedia data and the way it is actually stored and manipulated by a search engine.
Current generation of web applications have become more and more effective at aggregating massive amounts of data of different multimedia content, such as, pictures, videos, clips, paintings and mash-ups, and are capable of slicing and dicing it in different ways, as well as searching it and displaying it in an organized fashion, by using, for example, concept networks. A concept may enable understanding of a multimedia data from its related content. However, current art is unable to add any real “intelligence” to the mix, i.e., no new knowledge is extracted from the multimedia data that are aggregated by such systems. Moreover, the systems tend to be non-scalable due to the vast amounts of data they have to handle. This, by definition, hinders the ability to provide high quality searching for multimedia content.
There is therefore a need in the art to overcome the deficiencies of the prior art solutions and to provide the building element for a search engine for content-management of multimedia data that is intelligent, effective, and scalable.